• The Korean Journal of Applied Statistics
• /
• v.7 no.2
• /
• pp.201-211
• /
• 1994

Ignoring interaction effects has been pointed out to be one of serious drawbacks in analysis of the parameter designs which are constructed by using orthogonal arrays. In this paper a detecting procedure of influential 2-factor inteactions with minimum expeimental runs is described, when each contrl factor has two levels. The presented method is based on the near orthogonal arrays which are very similar to orthogonal arrays in the statistical structure. And those arrarys are the same as trace-optimal balanced saturated two-level fractional factorial designs of resolution V.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.9
• /
• pp.2191-2201
• /
• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.2
• /
• pp.134-146
• /
• 2009

Traffic classification seeks to assign packet flows to an appropriate quality of service(QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• The Korean Journal of Applied Statistics
• /
• v.1 no.1
• /
• pp.75-90
• /
• 1987

Statistical data analysis using the statisticaal packages can be performed on the personal computers. But it is not easy to select a personal computer in which statisticians could run statistical packages. The paper discusses some of the minimum requirements of the personal computers to use statistical packages and how to choose good statistical packages with better numerical results and introduces the statistical packages which are available in the personal computers.

• ETRI Journal
• /
• v.31 no.2
• /
• pp.162-172
• /
• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.1
• /
• pp.91-95
• /
• 2014

During a flood season, Bo region could be easily exposed to flood due to increase of ground water level and the water drain difficulty even the water amount of Bo can be managed. GFI for the flood risk is measured by mean depth to water during a dry season and minimum depth to water and tangent degree during a flood season. In this paper, a forecasting model of the target variable, GFI and predictors as differences of height between ground water and Bo water, distances from water resource, and soil characteristics are obtained for the dry season of 2012 and the flood season of 2012 with empirical data of Gangjungbo and Hamanbo. Obtained forecasting model would be used for keep the value of GFI below the maximum allowance for no flooding during flooding seasons with controlling the values of significant predictors.

• Communications for Statistical Applications and Methods
• /
• v.5 no.1
• /
• pp.265-275
• /
• 1998

In testing the partial association between two variables after controlling for the S levels of a third factor, the Mantel and Haenszel (1959) statistic is often used. Since the statistic is based on the asymptotic distribution of the sum X of S hypergeometric variates, a guideline for the minimum requirements for the application of the statistic is useful. Mantel and Fleiss (1980) developed a criterion based on the guideline for the Pearson's $X^2$ statistic. The criterion requires the distance from the expected value to the closer bound of X to be at least five. The Mantel-Fleiss (MF) criterion was studied through a simulation using the hypergeometric sampling scheme. The criterion is not satisfactory. The size of statistic exceeded nominal 0.05 level nearly 1/5 of the cases even when the criteion is met. However, the results show that the statistic is much more unstable and conservative when the criterion is not met.

• Journal of the Korean housing association
• /
• v.2 no.1
• /
• pp.1-12
• /
• 1991

The purpose of this study was to classify the types of housing life style. Housing life style was measured using four variables : furniture usage pattern, space usage pattern, family living pattern and heating system. A final Instrument was developed through the two stage pilot surveys. The respondents were 1,292 home-makers of the middle and high economic classes In Seoul and Daejeon, selected through stratified random sampling technique. Data were analyzed using SAS computer packages. The statistics used were frequency, percentage, Pear-3on`s correlation coefficient, Multiple Linear Regression, X2, and cluster analysis.The major findings were as follows : Five representative types of housing life style were found through cluster analysis. They were conventional minimum level life style, conventional optimum famiIy-centered life style, eclectic family-centered life style, contemporary optimum family - centered and contemporary so-cial, leasure-oriented life style.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.4
• /
• pp.371-378
• /
• 1999

This paper presents an efficient algorithm for loss reduction of distribution system by automatic sectionalizing switch operation in large scale distribution systems of radial type. Simulated Annealing algorithm among optimization techniques can avoid escape from local minima by accepting improvements in cost, but the use of this algorithm is also responsible for an excessive computation time requirement. To overcome this major limitation of Simulated Annealing algorithm, we may use advanced Simulated Annealing algorithm. All constaints are divided into two constraint group by using perturbation mechanism and penalty factor, so all trail solutions are feasible. The polynomial-time cooling schedule is used which is based on the statistics calculation during the search. This approaches results in saving CPU time. Numerical examples demonstrate the validity and effectiveness of the proposed methodology.

• Journal of Information Technology Applications and Management
• /
• v.24 no.3
• /
• pp.51-61
• /
• 2017

The objective of the study is to examine the acceptance and usability of Augmented Reality (AR) visuals developed for industry analysis of Thai Banks and whether these visuals can outperform the table of numbers in representing financial accounting data. Convenient samples were used and the data were collected with self-assessed questionnaires from 109 users with minimum prior experiences with financial analyses. The results from descriptive statistics indicates that despite having over 80% of respondents with little prior experience in analyzing financial performance of banking industry, the majority of them were able to correctly make prediction (96.4%), identify trend (82.6%) and compare banks' performance (70.6%). Their attitudes and perception towards Bank-AR visuals were above average. Although the overall usability score is average (53%), the respondents rated the Bank-AR visuals to be highly useful and had high intention to use them in the future.